diff --git a/GPy/models/uncertain_input_GP_regression.py b/GPy/models/uncertain_input_GP_regression.py
new file mode 100644
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+++ b/GPy/models/uncertain_input_GP_regression.py
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+# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
+
+import numpy as np
+import pylab as pb
+from ..util.linalg import mdot, jitchol, chol_inv, pdinv
+from ..util.plot import gpplot
+from .. import kern
+from ..inference.likelihoods import likelihood
+from GP_regression import GP_regression
+
+class uncertain_input_GP_regression(sparse_GP_regression):
+    """
+    Variational sparse GP model (Regression) with uncertainty on the inputs
+
+    :param X: inputs
+    :type X: np.ndarray (N x Q)
+    :param X_uncertainty: uncertainty on X (Gaussian variances, assumed isotrpic)
+    :type X_uncertainty: np.ndarray (N x Q)
+    :param Y: observed data
+    :type Y: np.ndarray of observations (N x D)
+    :param kernel : the kernel/covariance function. See link kernels
+    :type kernel: a GPy kernel
+    :param Z: inducing inputs (optional, see note)
+    :type Z: np.ndarray (M x Q) | None
+    :param Zslices: slices for the inducing inputs (see slicing TODO: link)
+    :param M : Number of inducing points (optional, default 10. Ignored if Z is not None)
+    :type M: int
+    :param beta: noise precision. TODO> ignore beta if doing EP
+    :type beta: float
+    :param normalize_(X|Y) : whether to normalize the data before computing (predictions will be in original scales)
+    :type normalize_(X|Y): bool
+    """
+
+    def __init__(self,X,Y,X_uncertainty,kernel=None, beta=100., Z=None,Zslices=None,M=10,normalize_X=False,normalize_Y=False):
+        sparse_GP_regression.__init__(self, X, Y, kernel = kernel, beta = beta, normalize_X = normalize_X, normalize_Y = normalize_Y)
+        self.trYYT = np.sum(np.square(self.Y))
+
+    def _compute_kernel_matrices(self):
+        # kernel computations, using BGPLVM notation
+        #TODO: slices for psi statistics (easy enough)
+        self.Kmm = self.kern.K(self.Z)
+        self.psi0 = self.kern.psi0(self.X,slices=self.Xslices).sum()
+        self.psi1 = self.kern.psi1(self.Z,self.X, self.X_uncertainty)
+        self.psi2 = self.kern.psi2(self.Z,self.X, self.X_uncertainty)
+
+    def dL_dtheta(self):
+        #re-cast computations in psi2 back to psi1:
+        dL_dtheta = self.kern.dK_dtheta(self.dL_dKmm,self.Z)
+        dL_dtheta += self.kern.dpsi0_dtheta(self.dL_dpsi0, self.Z,self.X,self.X_uncertainty)
+        dL_dtheta += self.kern.dpsi1_dtheta(self.dL_dpsi1,self.Z,self.X, self.X_uncertainty)
+        dL_dtheta += self.kern.dpsi2_dtheta(self.dL_dpsi2,self.Z,self.X, self.X_uncertainty)
+        return dL_dtheta
+
+    def dL_dZ(self):
+        dL_dZ = 2.*self.kern.dK_dX(self.dL_dKmm,self.Z,)#factor of two becase of vertical and horizontal 'stripes' in dKmm_dZ
+        dL_dZ += self.kern.dpsi1_dZ(dL_dpsi1,self.Z,self.X, self.X_uncertainty)
+        dL_dZ += self.kern.dpsi2_dZ(dL_dpsi2,self.Z,self.X, self.X_uncertainty)
+        return dL_dZ
+
+    def plot(self,*args,**kwargs):
+        """
+        Plot the fitted model: just call the sparse GP_regression plot function and then add
+        markers to represent uncertainty on the inputs
+        """
+        sparse_GP_regression.plot(self,*args,**kwargs)
+        if self.Q==1:
+            pb.errorbar(self.X[:,0], pb.ylim(0) ,xerr=2*np.sqrt(self.X_uncertainty.flatten()))
+